# Structural constrained virtual histology staining for human coronary imaging using deep learning

arXiv: https://arxiv.org/abs/2211.06737 · HTML: https://mlsanity.com/paper/2211.06737/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks](https://mlsanity.com/paper/1808.04538/) (2018)
- [Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning](https://mlsanity.com/paper/2204.11769/) (2022)

## Built on by (most cited first, 3 of 3)

- [DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images](https://mlsanity.com/paper/2410.03058/) (2024)
- [Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network](https://mlsanity.com/paper/2307.11130/) (2023)
- [SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images](https://mlsanity.com/paper/2307.12138/) (2023)

## Abstract (arXiv)

Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.